DB-MAGS: Multi-Anomaly Data Generation System for Transactional Databases

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Abstract

Existing database performance anomaly datasets have the problems of comprehensiveness in anomaly types, coarse-grained root causes, and unrealistic simulation for reproducing concurrent anomalies. To address these issues, we propose a data generation system tailored for Multi-Anomaly Reproduction in Databases (DB-MAGS). DBMAGS guarantees unified, authentic, and comprehensive data generation, while also providing fine-grained root causes. In the case of only a single anomaly occurred in the database, we categorize the factors affecting database performance anomalies, selective major categories of anomalies, and further subdivide each category into eighteen minor categories. This finer granularity of anomaly classification facilitates more specific and targeted anomaly remediation. For multiple anomalies simultaneously occurred in a database system, we categorize the relationships between anomalies into causal and concurrent, and enumerate different combinations of multiple anomalies, making the simulation of multiple anomaly scenarios more comprehensive and enhancing the diversity of generated data.